AI BriefingLiger Kernel Cuts GRPO Memory Usage by 40%
Key point
Liger Kernel has been integrated into TRL, reducing memory usage by 40% during GRPO training.
Details
Liger Kernel has been integrated into Hugging Face's TRL library, significantly improving GRPO (Group Relative Policy Optimization) training efficiency.
By applying the Liger Chunked Loss method, instead of storing all logits in memory during training, it computes them in chunks and accumulates gradients. This achieves a 40% reduction in peak memory usage without any degradation in model quality.
It also adds support for FSDP (Fully Sharded Data Parallel) and PEFT (Parameter-Efficient Fine-Tuning), making large-scale GRPO scaling across multiple GPUs and nodes even easier.
Users can instantly enable this feature by setting use_liger_loss=True in GRPOConfig.
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